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<oembed><version>1.0</version><provider_name>Microsoft Research</provider_name><provider_url>https://www.microsoft.com/en-us/research</provider_url><author_name>Chris Quirk</author_name><author_url>https://www.microsoft.com/en-us/research/people/chrisq/</author_url><title>Language to Code - Microsoft Research</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="guSZGsbNFn"&gt;&lt;a href="https://www.microsoft.com/en-us/research/project/language-to-code/"&gt;Language to Code&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/project/language-to-code/embed/#?secret=guSZGsbNFn" width="600" height="338" title="&#x201C;Language to Code&#x201D; &#x2014; Microsoft Research" data-secret="guSZGsbNFn" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script type="text/javascript"&gt;
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</html><description>Our goal is to let normal users tell computers what to do using normal language. This problem space is strongly related to natural language understanding, program synthesis, and many other areas. The&#xA0;data release associated with the following ACL publication LANGUAGE TO CODE: LEARNING SEMANTIC PARSERS FOR IF-THIS-THEN-THAT RECIPES Chris Quirk, Raymond Mooney and Michel Galley [&hellip;]</description></oembed>
